## Warning: NAs introduced by coercion
## Warning: NAs introduced by coercion
## Warning: NAs introduced by coercion
## Warning: NAs introduced by coercion
## Warning: NAs introduced by coercion
## Warning: NAs introduced by coercion
## Warning: NAs introduced by coercion
sem_rt$pathlengthfac = ordered(as.factor(as.character(sem_rt$pathlength)),
levels = c("1", "2", "3", "4", "6", "15"))
sem_rt$subject = as.factor(sem_rt$subject)
rt_aov = aov(data = sem_rt, rt ~ pathlengthfac +
Error(subject/(pathlengthfac)))
summary(rt_aov)
##
## Error: subject
## Df Sum Sq Mean Sq F value Pr(>F)
## Residuals 39 13728805 352021
##
## Error: subject:pathlengthfac
## Df Sum Sq Mean Sq F value Pr(>F)
## pathlengthfac 5 110141 22028 3.52 0.00454 **
## Residuals 195 1220328 6258
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
z_sem_rt$pathlengthfac = ordered(as.factor(as.character(z_sem_rt$pathlength)),
levels = c("1", "2", "3", "4", "6", "15"))
z_sem_rt$subject = as.factor(z_sem_rt$subject)
z_rt_aov = aov(data = z_sem_rt, zRT_trim ~ pathlengthfac +
Error(subject/(pathlengthfac)))
summary(z_rt_aov)
##
## Error: subject
## Df Sum Sq Mean Sq F value Pr(>F)
## Residuals 39 0.005412 0.0001388
##
## Error: subject:pathlengthfac
## Df Sum Sq Mean Sq F value Pr(>F)
## pathlengthfac 5 2.143 0.4287 9.764 2.43e-08 ***
## Residuals 195 8.561 0.0439
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
z_sem_rt %>%
ggplot(aes(x = pathlength, y = zRT_trim))+
geom_point(color = "black", size = 1)+
geom_smooth(method = "loess", color = "red")+
#geom_errorbar(aes(ymin=Trials - ci, ymax=Trials + ci),
# width=.2, color = "gray26",
# position = position_dodge(0.7))+
theme_few()+
# scale_x_continuous(breaks = c(1,2,3,4,5,6,10,15,20))+
xlab("Path Length") + ylab("z-RT") +
ggtitle("z-RT for Relatedness Judgments") +
# facet_wrap(~subject)+
theme(axis.text = element_text(size = rel(1)),
axis.title = element_text(face = "bold", size = rel(1)),
legend.title = element_text(face = "bold", size = rel(1)),
plot.title = element_text(hjust = .5),
strip.text.x = element_text(face = "bold", size = rel(1.4)))
## Loading required package: Matrix
## Linear mixed model fit by REML ['lmerMod']
## Formula: rt ~ 1 + (1 | subject) + (1 | trial_index) + (1 | prime_word) +
## (1 | target_word)
## Data: new_sem_z
##
## REML criterion at convergence: 121468.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9975 -0.5992 -0.1583 0.4368 5.8497
##
## Random effects:
## Groups Name Variance Std.Dev.
## target_word (Intercept) 587.4 24.24
## prime_word (Intercept) 914.1 30.23
## trial_index (Intercept) 124.9 11.18
## subject (Intercept) 23886.8 154.55
## Residual 41893.2 204.68
## Number of obs: 8973, groups:
## target_word, 1918; prime_word, 1918; trial_index, 240; subject, 40
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 697.88 24.57 28.41
## [1] 0.3784984
## Linear mixed model fit by REML ['lmerMod']
## Formula: zRT_trim ~ pathlengthfac + (1 | subject)
## Data: new_sem_z
##
## REML criterion at convergence: 25378.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.5287 -0.7314 -0.2061 0.5651 3.9283
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 3.626e-32 1.904e-16
## Residual 9.875e-01 9.938e-01
## Number of obs: 8973, groups: subject, 40
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.12320 0.02579 4.777
## pathlengthfac1 -0.15767 0.03645 -4.326
## pathlengthfac3 -0.02672 0.03632 -0.736
## pathlengthfac4 -0.09787 0.03643 -2.687
## pathlengthfac5 -0.18629 0.03641 -5.117
## pathlengthfac6 -0.27000 0.03636 -7.426
##
## Correlation of Fixed Effects:
## (Intr) pthln1 pthln3 pthln4 pthln5
## pthlngthfc1 -0.708
## pthlngthfc3 -0.710 0.502
## pthlngthfc4 -0.708 0.501 0.503
## pthlngthfc5 -0.708 0.501 0.503 0.501
## pthlngthfc6 -0.709 0.502 0.504 0.502 0.502
## Linear mixed model fit by REML ['lmerMod']
## Formula: rt ~ pathlengthfac + (pathlengthfac | subject)
## Data: sem
##
## REML criterion at convergence: 144701.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.951 -0.351 -0.115 0.184 34.778
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subject (Intercept) 66078 257.06
## pathlengthfac2 1530 39.11 0.56
## pathlengthfac3 12139 110.18 -0.57 0.34
## pathlengthfac4 13916 117.96 -0.37 0.41 0.92
## pathlengthfac5 7966 89.25 -0.56 0.36 1.00 0.92
## pathlengthfac6 5580 74.70 -0.17 0.71 0.84 0.70 0.84
## Residual 202173 449.64
## Number of obs: 9600, groups: subject, 40
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 732.331 42.170 17.366
## pathlengthfac2 30.535 17.058 1.790
## pathlengthfac3 5.857 23.584 0.248
## pathlengthfac4 21.454 24.507 0.875
## pathlengthfac5 -12.491 21.257 -0.588
## pathlengthfac6 -34.499 19.805 -1.742
##
## Correlation of Fixed Effects:
## (Intr) pthln2 pthln3 pthln4 pthln5
## pthlngthfc2 0.019
## pthlngthfc3 -0.531 0.406
## pthlngthfc4 -0.394 0.416 0.736
## pthlngthfc5 -0.496 0.435 0.742 0.709
## pthlngthfc6 -0.249 0.528 0.640 0.580 0.635
## Adding ELP covariates
elp_model = lmer(data = new_sem_z, rt ~ mean_len + mean_logf +
mean_ldtz +
(1|subject) + (1|trial_index))
fit_from_elp = broom::augment(elp_model,new_sem_z)
contrasts(fit_from_elp$pathlengthfac) = contr.treatment(6, base = 2)
m1_fixed_elp = lmer(data = fit_from_elp, .resid ~ pathlengthfac +
(1|subject) + (1|trial_index))
summary(m1_fixed_elp)
## Linear mixed model fit by REML ['lmerMod']
## Formula: .resid ~ pathlengthfac + (1 | subject) + (1 | trial_index)
## Data: fit_from_elp
##
## REML criterion at convergence: 121039.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0866 -0.6125 -0.1655 0.4418 6.1215
##
## Random effects:
## Groups Name Variance Std.Dev.
## trial_index (Intercept) 8.186e-10 2.861e-05
## subject (Intercept) 0.000e+00 0.000e+00
## Residual 4.268e+04 2.066e+02
## Number of obs: 8969, groups: trial_index, 240; subject, 40
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 25.228 5.361 4.706
## pathlengthfac1 -29.957 7.577 -3.954
## pathlengthfac3 -11.585 7.552 -1.534
## pathlengthfac4 -20.829 7.578 -2.749
## pathlengthfac5 -37.177 7.569 -4.912
## pathlengthfac6 -51.635 7.559 -6.831
##
## Correlation of Fixed Effects:
## (Intr) pthln1 pthln3 pthln4 pthln5
## pthlngthfc1 -0.708
## pthlngthfc3 -0.710 0.502
## pthlngthfc4 -0.707 0.501 0.502
## pthlngthfc5 -0.708 0.501 0.503 0.501
## pthlngthfc6 -0.709 0.502 0.504 0.502 0.502
m1_all_elp = lmer(data = new_sem_z, rt ~ pathlengthfac +
mean_len + mean_logf + mean_ldtz +
(1|subject) + (1|trial_index))
summary(m1_all_elp)
## Linear mixed model fit by REML ['lmerMod']
## Formula: rt ~ pathlengthfac + mean_len + mean_logf + mean_ldtz + (1 |
## subject) + (1 | trial_index)
## Data: new_sem_z
##
## REML criterion at convergence: 121309.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0679 -0.6095 -0.1639 0.4401 6.0992
##
## Random effects:
## Groups Name Variance Std.Dev.
## trial_index (Intercept) 178.6 13.36
## subject (Intercept) 23884.6 154.55
## Residual 42987.9 207.34
## Number of obs: 8969, groups: trial_index, 240; subject, 40
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 690.45896 31.66869 21.803
## pathlengthfac1 -30.18356 7.62546 -3.958
## pathlengthfac3 -11.60839 7.60125 -1.527
## pathlengthfac4 -20.96165 7.62511 -2.749
## pathlengthfac5 -37.44405 7.61759 -4.915
## pathlengthfac6 -51.99523 7.61151 -6.831
## mean_len 5.74846 1.74779 3.289
## mean_logf 0.09196 1.99780 0.046
## mean_ldtz 5.32761 16.65093 0.320
##
## Correlation of Fixed Effects:
## (Intr) pthln1 pthln3 pthln4 pthln5 pthln6 men_ln mn_lgf
## pthlngthfc1 -0.133
## pthlngthfc3 -0.143 0.502
## pthlngthfc4 -0.139 0.501 0.502
## pthlngthfc5 -0.142 0.502 0.503 0.501
## pthlngthfc6 -0.152 0.502 0.503 0.502 0.503
## mean_len -0.388 -0.003 0.003 0.011 0.019 0.037
## mean_logf -0.391 0.039 0.034 0.030 0.033 0.036 -0.109
## mean_ldtz 0.108 0.032 -0.008 0.005 0.013 0.005 -0.464 0.527
fixed.frame <-
data.frame(expand.grid( pathlengthfac = c("1","2", "3",
"4", "6", "15"))) %>%
mutate(pred = predict(m1_fixed_elp, newdata = ., re.form = NA))
fixed.frame %>%
mutate(Pathlength = factor(pathlengthfac,
levels = unique(pathlengthfac),
labels = c("1","2", "3",
"4", "6", "15")))%>%
ggplot(aes(x = Pathlength, y = pred, group = 1))+
geom_point()+
# geom_smooth(method = "loess")+
geom_line(color = "green")+
theme_few()+
xlab("Path Length") + ylab("RT residuals ") +
ggtitle("z-RT for Relatedness Judgments") +
theme(axis.text = element_text(size = rel(1)),
axis.title = element_text(face = "bold", size = rel(1)),
legend.title = element_text(face = "bold", size = rel(1)),
plot.title = element_text(hjust = .5),
strip.text.x = element_text(face = "bold", size = rel(1.4)))
elpnorms = read.csv("ELP_norms.csv", header = TRUE, sep = ",")
elpnorms = elpnorms[,c(1,2)]
colnames(elpnorms) = c("prime_word", "prime_concreteness")
elpnorms$prime_word = toupper(elpnorms$prime_word)
elpnorms$prime_word = paste(elpnorms$prime_word, "")
elpnorms$prime_word = as.character(elpnorms$prime_word)
sem$prime_word = as.character(sem$prime_word)
merged_sem_prime= inner_join(sem, elpnorms, by = "prime_word")
merged_sem_prime = merged_sem_prime[,c(4,7,15,37)]
colnames(elpnorms) = c("target_word", "target_concreteness")
sem$target_word = as.character(sem$target_word)
merged_sem_target= inner_join(sem, elpnorms, by = "target_word")
merged_sem_target = merged_sem_target[,c(4,7, 16,37)]
merged_concretness = full_join(merged_sem_prime, merged_sem_target,
by = c("trial_index", "subject"))
merged_concretness$mean_conc = (merged_concretness$prime_concreteness +
merged_concretness$target_concreteness) / 2
### NOW WE HAVE CONCRETENESS NORMS FOR ALL ITEMS IN THE DATASET
### NEED TO COMBINE THIS WITH ACTUAL SEM DATA
new_sem_z$prime_word = as.character(new_sem_z$prime_word)
new_sem_z$target_word = as.character(new_sem_z$target_word)
final_sem = inner_join(new_sem_z, merged_concretness,
by = c("trial_index", "subject",
"prime_word", "target_word") )
final_sem$pathlengthfac = ordered(as.factor(as.character(final_sem$pathlength)),
levels = c("1", "2", "3", "4", "6","15"))
elp_model = lmer(data = final_sem, zRT_trim ~ mean_len + mean_logf +
mean_ldtz + mean_conc +
(1|subject) + (1|trial_index))
fit_from_elp = broom::augment(elp_model,final_sem)
contrasts(fit_from_elp$pathlengthfac) = contr.treatment(6, base = 2)
m1_fixed_elp = lmer(data = fit_from_elp, .resid ~ pathlengthfac +
(1|subject) + (1|trial_index))
summary(m1_fixed_elp)
## Linear mixed model fit by REML ['lmerMod']
## Formula: .resid ~ pathlengthfac + (1 | subject) + (1 | trial_index)
## Data: fit_from_elp
##
## REML criterion at convergence: 22999.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.5403 -0.7231 -0.2069 0.5713 4.1219
##
## Random effects:
## Groups Name Variance Std.Dev.
## trial_index (Intercept) 9.132e-15 9.556e-08
## subject (Intercept) 0.000e+00 0.000e+00
## Residual 9.744e-01 9.871e-01
## Number of obs: 8170, groups: trial_index, 240; subject, 40
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.12752 0.02710 4.706
## pathlengthfac1 -0.16409 0.03803 -4.315
## pathlengthfac3 -0.02534 0.03813 -0.665
## pathlengthfac4 -0.10481 0.03781 -2.772
## pathlengthfac5 -0.17793 0.03835 -4.640
## pathlengthfac6 -0.28701 0.03784 -7.584
##
## Correlation of Fixed Effects:
## (Intr) pthln1 pthln3 pthln4 pthln5
## pthlngthfc1 -0.713
## pthlngthfc3 -0.711 0.506
## pthlngthfc4 -0.717 0.511 0.509
## pthlngthfc5 -0.707 0.503 0.502 0.506
## pthlngthfc6 -0.716 0.510 0.509 0.513 0.506
contrasts(final_sem$pathlengthfac) = contr.treatment(6, base = 2)
m1_all_elp = lme4::lmer(data = final_sem, zRT_trim ~ pathlengthfac*Type +
mean_len + mean_logf + mean_ldtz + mean_conc +
(1|subject) + (1|trial_index) +
+ (1|target_word))
summary(m1_all_elp)
## Linear mixed model fit by REML ['lmerMod']
## Formula:
## zRT_trim ~ pathlengthfac * Type + mean_len + mean_logf + mean_ldtz +
## mean_conc + (1 | subject) + (1 | trial_index) + +(1 | target_word)
## Data: final_sem
##
## REML criterion at convergence: 23050.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.6093 -0.7039 -0.1884 0.5536 4.0610
##
## Random effects:
## Groups Name Variance Std.Dev.
## target_word (Intercept) 0.032611 0.18059
## trial_index (Intercept) 0.007258 0.08519
## subject (Intercept) 0.000000 0.00000
## Residual 0.939300 0.96918
## Number of obs: 8170, groups:
## target_word, 1741; trial_index, 240; subject, 40
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.513120 0.144977 3.539
## pathlengthfac1 -0.216878 0.053472 -4.056
## pathlengthfac3 0.158169 0.066902 2.364
## pathlengthfac4 -0.006934 0.068349 -0.101
## pathlengthfac5 0.150777 0.087102 1.731
## pathlengthfac6 -0.061106 0.094001 -0.650
## TypeUnrelated -0.065704 0.055128 -1.192
## mean_len 0.018830 0.009514 1.979
## mean_logf -0.018096 0.011248 -1.609
## mean_ldtz -0.012876 0.089039 -0.145
## mean_conc -0.083593 0.014986 -5.578
## pathlengthfac1:TypeUnrelated 0.118195 0.079853 1.480
## pathlengthfac3:TypeUnrelated -0.240884 0.082009 -2.937
## pathlengthfac4:TypeUnrelated -0.118733 0.082758 -1.435
## pathlengthfac5:TypeUnrelated -0.356285 0.098469 -3.618
## pathlengthfac6:TypeUnrelated -0.231131 0.104158 -2.219
##
## Correlation matrix not shown by default, as p = 16 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it
mean_length = mean(final_sem$mean_len, na.rm = TRUE)
mean_logfreq = mean(final_sem$mean_logf, na.rm = TRUE)
mean_lexdec = mean(final_sem$mean_ldtz, na.rm = TRUE)
mean_concreteness = mean(final_sem$mean_conc, na.rm = TRUE)
fixed.frame <-
data.frame(expand.grid( pathlengthfac = c("1","2", "3",
"4", "6", "15"),
Type= c("Related", "Unrelated"),
mean_len = mean_length,
mean_logf = mean_logfreq,
mean_ldtz = mean_lexdec,
mean_conc = mean_concreteness)) %>%
mutate(pred = predict(m1_all_elp, newdata = ., re.form = NA))
fixed.frame %>%
mutate(Pathlength = factor(pathlengthfac,
levels = unique(pathlengthfac),
labels = c("1","2", "3",
"4", "6", "15")))%>%
ggplot(aes(x = Pathlength, y = pred, group = Type, color = Type))+
geom_point()+
geom_line()+
# geom_smooth(method = "loess")+
#geom_line(color = "green")+
theme_few()+
xlab("Path Length") + ylab("z-scored RT") +
ggtitle("z-scored RT for Relatedness Judgments") +
theme(axis.text = element_text(size = rel(1)),
axis.title = element_text(face = "bold", size = rel(1)),
legend.title = element_text(face = "bold", size = rel(1)),
plot.title = element_text(hjust = .5),
strip.text.x = element_text(face = "bold", size = rel(1.4)))
## TESTING QUADRATIC TREND
m3_linear = lmer(data = final_sem, zRT_trim~ pathlength +
mean_len + mean_logf + mean_ldtz + mean_conc +
(1|subject) + (1|trial_index) +
+ (1|target_word))
summary(m3_linear)
## Linear mixed model fit by REML ['lmerMod']
## Formula:
## zRT_trim ~ pathlength + mean_len + mean_logf + mean_ldtz + mean_conc +
## (1 | subject) + (1 | trial_index) + +(1 | target_word)
## Data: final_sem
##
## REML criterion at convergence: 23115
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.7061 -0.7092 -0.1986 0.5474 3.9650
##
## Random effects:
## Groups Name Variance Std.Dev.
## target_word (Intercept) 0.041142 0.20283
## trial_index (Intercept) 0.007316 0.08554
## subject (Intercept) 0.000000 0.00000
## Residual 0.942842 0.97100
## Number of obs: 8170, groups:
## target_word, 1741; trial_index, 240; subject, 40
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.426239 0.141600 3.010
## pathlength -0.016265 0.002474 -6.575
## mean_len 0.021271 0.009662 2.202
## mean_logf -0.016994 0.011427 -1.487
## mean_ldtz -0.023441 0.090454 -0.259
## mean_conc -0.090186 0.015203 -5.932
##
## Correlation of Fixed Effects:
## (Intr) pthlng men_ln mn_lgf mn_ldt
## pathlength -0.117
## mean_len -0.599 0.045
## mean_logf -0.659 0.014 -0.022
## mean_ldtz 0.038 -0.008 -0.403 0.541
## mean_conc -0.665 -0.011 0.209 0.314 0.112
m3_quad = lmer(data = final_sem, zRT_trim ~ pathlength + I((pathlength)^2) +
mean_len + mean_logf + mean_ldtz + mean_conc +
(1|subject) + (1|trial_index) +
+ (1|target_word))
summary(m3_quad)
## Linear mixed model fit by REML ['lmerMod']
## Formula:
## zRT_trim ~ pathlength + I((pathlength)^2) + mean_len + mean_logf +
## mean_ldtz + mean_conc + (1 | subject) + (1 | trial_index) +
## +(1 | target_word)
## Data: final_sem
##
## REML criterion at convergence: 23127
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.6941 -0.7088 -0.1992 0.5477 3.9580
##
## Random effects:
## Groups Name Variance Std.Dev.
## target_word (Intercept) 0.041064 0.20264
## trial_index (Intercept) 0.007354 0.08575
## subject (Intercept) 0.000000 0.00000
## Residual 0.942921 0.97104
## Number of obs: 8170, groups:
## target_word, 1741; trial_index, 240; subject, 40
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.3981975 0.1458140 2.731
## pathlength -0.0066325 0.0122529 -0.541
## I((pathlength)^2) -0.0005749 0.0007162 -0.803
## mean_len 0.0215773 0.0096681 2.232
## mean_logf -0.0167243 0.0114309 -1.463
## mean_ldtz -0.0238290 0.0904468 -0.263
## mean_conc -0.0896771 0.0152139 -5.894
##
## Correlation of Fixed Effects:
## (Intr) pthlng I(()^2 men_ln mn_lgf mn_ldt
## pathlength -0.257
## I((pthl)^2) 0.239 -0.979
## mean_len -0.590 0.047 -0.039
## mean_logf -0.647 0.031 -0.029 -0.020
## mean_ldtz 0.038 -0.007 0.005 -0.403 0.540
## mean_conc -0.655 0.038 -0.041 0.210 0.315 0.111
anova(m3_linear, m3_quad)
## refitting model(s) with ML (instead of REML)
## Data: final_sem
## Models:
## m3_linear: zRT_trim ~ pathlength + mean_len + mean_logf + mean_ldtz + mean_conc +
## m3_linear: (1 | subject) + (1 | trial_index) + +(1 | target_word)
## m3_quad: zRT_trim ~ pathlength + I((pathlength)^2) + mean_len + mean_logf +
## m3_quad: mean_ldtz + mean_conc + (1 | subject) + (1 | trial_index) +
## m3_quad: +(1 | target_word)
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## m3_linear 10 23093 23163 -11537 23073
## m3_quad 11 23094 23172 -11536 23072 0.6467 1 0.4213
sjPlot::sjp.lm(m3_quad, type = "poly", poly.term = "pathlength")
## `sjp.lm()` will become deprecated in the future. Please use `plot_model()` instead.
items_kenett = group_by(final_sem, pathlength) %>%
summarise(items = n())
items_kenett$pathlength = as.factor(items_kenett$pathlength)
ggplot(items_kenett, aes(x = pathlength, y = items))+
geom_bar(stat = "identity", position = "dodge", width = 0.7, color= "black")+
theme_few()+
xlab("Kenett Path Length") + ylab("Number of Items") +
ggtitle("Kenett Item Distribution") +
theme(axis.text = element_text(size = rel(1)),
axis.title = element_text(face = "bold", size = rel(1)),
legend.title = element_text(face = "bold", size = rel(1)),
plot.title = element_text(hjust = .5),
strip.text.x = element_text(face = "bold", size = rel(1.4)))
items_undirected = group_by(final_sem, undirected) %>%
summarise(items = n())
items_undirected_subject = group_by(final_sem, subject, undirected) %>%
summarise(items = n())
undirected_rmisc = Rmisc::summarySE(items_undirected_subject,
measurevar = "items",
groupvars = c("undirected"))
final_sem$undirectedfac = ordered(as.factor(as.character(final_sem$undirected)),
levels = c("1", "2", "3", "4"))
contrasts(final_sem$undirectedfac) = contr.treatment(4, base = 2)
m_undirected = lmer(data = final_sem, zRT_trim~ undirectedfac*Type +
mean_len + mean_logf + mean_ldtz + mean_conc +
(1|subject) + (1|trial_index) +
+ (1|target_word))
summary(m_undirected)
## Linear mixed model fit by REML ['lmerMod']
## Formula:
## zRT_trim ~ undirectedfac * Type + mean_len + mean_logf + mean_ldtz +
## mean_conc + (1 | subject) + (1 | trial_index) + +(1 | target_word)
## Data: final_sem
##
## REML criterion at convergence: 23023.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8245 -0.7128 -0.1948 0.5497 4.1351
##
## Random effects:
## Groups Name Variance Std.Dev.
## target_word (Intercept) 0.031985 0.17884
## trial_index (Intercept) 0.006751 0.08217
## subject (Intercept) 0.000000 0.00000
## Residual 0.938698 0.96886
## Number of obs: 8170, groups:
## target_word, 1741; trial_index, 240; subject, 40
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.576026 0.141109 4.082
## undirectedfac1 -0.433002 0.051167 -8.462
## undirectedfac3 0.033574 0.051731 0.649
## undirectedfac4 -0.268627 0.185574 -1.448
## TypeUnrelated -0.173571 0.034809 -4.986
## mean_len 0.019052 0.009492 2.007
## mean_logf -0.013732 0.011234 -1.222
## mean_ldtz 0.021990 0.089317 0.246
## mean_conc -0.096772 0.014951 -6.473
## undirectedfac1:TypeUnrelated 0.395010 0.093843 4.209
## undirectedfac3:TypeUnrelated -0.144225 0.059005 -2.444
## undirectedfac4:TypeUnrelated 0.031456 0.191528 0.164
##
## Correlation of Fixed Effects:
## (Intr) undrc1 undrc3 undrc4 TypUnr men_ln mn_lgf mn_ldt mn_cnc
## undirctdfc1 -0.105
## undirctdfc3 -0.111 0.268
## undirctdfc4 -0.038 0.074 0.075
## TypeUnreltd -0.169 0.380 0.375 0.104
## mean_len -0.595 0.012 -0.001 -0.002 0.059
## mean_logf -0.647 -0.028 0.000 -0.006 0.007 -0.022
## mean_ldtz 0.043 0.016 0.006 -0.034 -0.003 -0.403 0.538
## mean_conc -0.658 0.070 0.045 0.016 0.002 0.207 0.309 0.111
## undrctd1:TU 0.074 -0.510 -0.140 -0.039 -0.371 -0.015 -0.026 -0.016 -0.007
## undrctd3:TU 0.091 -0.228 -0.863 -0.063 -0.587 -0.009 0.001 -0.012 -0.028
## undrctd4:TU 0.030 -0.070 -0.070 -0.962 -0.181 0.005 -0.001 0.006 -0.010
## un1:TU un3:TU
## undirctdfc1
## undirctdfc3
## undirctdfc4
## TypeUnreltd
## mean_len
## mean_logf
## mean_ldtz
## mean_conc
## undrctd1:TU
## undrctd3:TU 0.219
## undrctd4:TU 0.068 0.107
ggplot(undirected_rmisc, aes(x = undirected, y = items))+
geom_bar(stat = "identity", position = "dodge", width = 0.7, color= "black")+
theme_few()+
xlab("Non-directed Path Length") + ylab("Number of Items") +
ggtitle("Non-Directed Item Distribution") +
theme(axis.text = element_text(size = rel(1)),
axis.title = element_text(face = "bold", size = rel(1)),
legend.title = element_text(face = "bold", size = rel(1)),
plot.title = element_text(hjust = .5),
strip.text.x = element_text(face = "bold", size = rel(1.4)))
mean_length = mean(final_sem$mean_len, na.rm = TRUE)
mean_logfreq = mean(final_sem$mean_logf, na.rm = TRUE)
mean_lexdec = mean(final_sem$mean_ldtz, na.rm = TRUE)
mean_concreteness = mean(final_sem$mean_conc, na.rm = TRUE)
fixed.frame <-
data.frame(expand.grid( undirectedfac = c("1", "2", "3", "4"),
Type = c("Related", "Unrelated"),
mean_len = mean_length,
mean_logf = mean_logfreq,
mean_ldtz = mean_lexdec,
mean_conc = mean_concreteness)) %>%
mutate(pred = predict(m_undirected, newdata = ., re.form = NA))
fixed.frame %>%
mutate(Pathlength = factor(undirectedfac,
levels = unique(undirectedfac),
labels = c("1","2", "3","4")))%>%
ggplot(aes(x = Pathlength, y = pred, group = Type, color = Type))+
geom_point()+
# geom_smooth(method = "loess")+
geom_line()+
theme_few()+
xlab("Path Length") + ylab("z-scored RT") +
ggtitle("z-scored RT for Relatedness Judgments") +
theme(axis.text = element_text(size = rel(1)),
axis.title = element_text(face = "bold", size = rel(1)),
legend.title = element_text(face = "bold", size = rel(1)),
plot.title = element_text(hjust = .5),
strip.text.x = element_text(face = "bold", size = rel(1.4)))
final_sem$newdirected = ifelse(final_sem$directed == "Inf" |
final_sem$directed == "NA", NA,
final_sem$directed)
final_sem$directedcollapsed = ifelse((final_sem$newdirected == "5" |
final_sem$newdirected == "6" |
final_sem$newdirected == "7" |
final_sem$newdirected == "8"), "H",
final_sem$newdirected)
items_directed = group_by(final_sem, newdirected) %>%
summarise(items = n())
items_directed_subject = group_by(final_sem, subject, newdirected) %>%
summarise(items = n())
directed_rmisc = Rmisc::summarySE(items_directed_subject,
measurevar = "items",
groupvars = c("newdirected"))
final_sem$directedfac =
ordered(as.factor(as.character(final_sem$newdirected)),
levels = c("1", "2", "3", "4", "5",
"6", "7", "8"))
contrasts(final_sem$directedfac) = contr.treatment(8, base = 2)
final_sem$collapsedfac =
ordered(as.factor(as.character(final_sem$directedcollapsed)),
levels = c("1", "2", "3", "4", "H"))
contrasts(final_sem$collapsedfac) = contr.treatment(5, base = 2)
m_directed = lme4::lmer(data = final_sem, zRT_trim ~ collapsedfac*Type +
mean_len + mean_logf + mean_ldtz + mean_conc +
(1|subject) + (1|trial_index) +
+ (1|target_word))
summary(m_directed)
## Linear mixed model fit by REML ['lmerMod']
## Formula:
## zRT_trim ~ collapsedfac * Type + mean_len + mean_logf + mean_ldtz +
## mean_conc + (1 | subject) + (1 | trial_index) + +(1 | target_word)
## Data: final_sem
##
## REML criterion at convergence: 22180
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.7216 -0.7076 -0.1930 0.5531 4.0088
##
## Random effects:
## Groups Name Variance Std.Dev.
## target_word (Intercept) 0.035101 0.18735
## trial_index (Intercept) 0.007171 0.08468
## subject (Intercept) 0.000000 0.00000
## Residual 0.936141 0.96754
## Number of obs: 7866, groups:
## target_word, 1673; trial_index, 240; subject, 40
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.506507 0.150197 3.372
## collapsedfac1 -0.354059 0.065641 -5.394
## collapsedfac3 0.114629 0.058779 1.950
## collapsedfac4 0.098296 0.058606 1.677
## collapsedfac5 0.152836 0.065456 2.335
## TypeUnrelated -0.052784 0.057540 -0.917
## mean_len 0.015727 0.009738 1.615
## mean_logf -0.014750 0.011563 -1.276
## mean_ldtz 0.030201 0.091877 0.329
## mean_conc -0.098539 0.015394 -6.401
## collapsedfac1:TypeUnrelated 0.363319 0.120918 3.005
## collapsedfac3:TypeUnrelated -0.111086 0.078230 -1.420
## collapsedfac4:TypeUnrelated -0.200311 0.075843 -2.641
## collapsedfac5:TypeUnrelated -0.334690 0.081757 -4.094
##
## Correlation matrix not shown by default, as p = 14 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it
items_directed$newdirected = as.factor(items_directed$newdirected)
ggplot(directed_rmisc, aes(x = newdirected, y = items))+
geom_bar(stat = "identity", position = "dodge", width = 0.7, color= "black")+
theme_few()+
xlab("Directed Path Length") + ylab("Number of Items") +
ggtitle("Directed Item Distribution") +
theme(axis.text = element_text(size = rel(1)),
axis.title = element_text(face = "bold", size = rel(1)),
legend.title = element_text(face = "bold", size = rel(1)),
plot.title = element_text(hjust = .5),
strip.text.x = element_text(face = "bold", size = rel(1.4)))
## Warning: Removed 1 rows containing missing values (geom_bar).
### Plot Directed
mean_length = mean(final_sem$mean_len, na.rm = TRUE)
mean_logfreq = mean(final_sem$mean_logf, na.rm = TRUE)
mean_lexdec = mean(final_sem$mean_ldtz, na.rm = TRUE)
mean_concreteness = mean(final_sem$mean_conc, na.rm = TRUE)
fixed.frame <-
data.frame(expand.grid( collapsedfac = c("1", "2", "3", "4", "H"),
Type = c("Related", "Unrelated"),
mean_len = mean_length,
mean_logf = mean_logfreq,
mean_ldtz = mean_lexdec,
mean_conc = mean_concreteness)) %>%
mutate(pred = predict(m_directed, newdata = ., re.form = NA))
# fixed.frame <-
# data.frame(expand.grid( newdirected =
# seq(min(final_sem$newdirected, na.rm = TRUE),
# max(final_sem$newdirected, na.rm = TRUE),
# 1),
# mean_len = mean_length,
# mean_logf = mean_logfreq,
# mean_ldtz = mean_lexdec,
# mean_conc = mean_concreteness)) %>%
# mutate(pred = predict(m_directed, newdata = ., re.form = NA))
fixed.frame %>%
mutate(Pathlength = factor(collapsedfac,
levels = unique(collapsedfac),
labels = c("1","2", "3","4", "H")))%>%
ggplot(aes(x = collapsedfac, y = pred, group = Type, color = Type))+
geom_point()+
# geom_smooth(method = "loess")+
geom_line()+
theme_few()+
xlab("Path Length") + ylab("z-scored RT") +
ggtitle("z-scored RT for Relatedness Judgments") +
theme(axis.text = element_text(size = rel(1)),
axis.title = element_text(face = "bold", size = rel(1)),
legend.title = element_text(face = "bold", size = rel(1)),
plot.title = element_text(hjust = .5),
strip.text.x = element_text(face = "bold", size = rel(1.4)))
## Log P
final_sem$logp = round(final_sem$logp, 2)
final_sem$newlogp = ifelse(final_sem$logp == "Inf" |
final_sem$logp == "NA", NA,
final_sem$logp)
final_sem$roundedlogp = round(final_sem$newlogp, 0)
items_logp = group_by(final_sem, roundedlogp) %>%
summarise(items = n())
items_logp_subject = group_by(final_sem, subject, roundedlogp) %>%
summarise(items = n())
logp_rmisc = Rmisc::summarySE(items_logp_subject,
measurevar = "items",
groupvars = c("roundedlogp"))
m_logp = lmer(data = final_sem, zRT_trim ~ newlogp +
mean_len + mean_logf + mean_ldtz + mean_conc +
(1|subject) + (1|trial_index) +
+ (1|target_word))
summary(m_logp)
## Linear mixed model fit by REML ['lmerMod']
## Formula:
## zRT_trim ~ newlogp + mean_len + mean_logf + mean_ldtz + mean_conc +
## (1 | subject) + (1 | trial_index) + +(1 | target_word)
## Data: final_sem
##
## REML criterion at convergence: 21104.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.6759 -0.7064 -0.2079 0.5514 3.9516
##
## Random effects:
## Groups Name Variance Std.Dev.
## target_word (Intercept) 0.04587 0.21417
## trial_index (Intercept) 0.00858 0.09263
## subject (Intercept) 0.00000 0.00000
## Residual 0.94299 0.97107
## Number of obs: 7444, groups:
## target_word, 1673; trial_index, 240; subject, 38
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.371358 0.158249 2.347
## newlogp -0.001984 0.002003 -0.991
## mean_len 0.020905 0.010226 2.044
## mean_logf -0.013852 0.012126 -1.142
## mean_ldtz 0.024448 0.096560 0.253
## mean_conc -0.089674 0.016153 -5.551
##
## Correlation of Fixed Effects:
## (Intr) newlgp men_ln mn_lgf mn_ldt
## newlogp -0.300
## mean_len -0.579 -0.005
## mean_logf -0.654 0.060 -0.006
## mean_ldtz 0.068 -0.096 -0.403 0.525
## mean_conc -0.666 0.079 0.219 0.332 0.109
items_logp$roundedlogp = as.factor(items_logp$roundedlogp)
ggplot(items_logp, aes(x = roundedlogp, y = items))+
geom_bar(stat = "identity", position = "dodge", width = 0.2, color= "black")+
theme_few()+
xlab("Directed Path Length") + ylab("Number of Items") +
ggtitle("Log P Item Distribution") +
theme(axis.text = element_text(size = rel(1)),
axis.title = element_text(face = "bold", size = rel(1)),
legend.title = element_text(face = "bold", size = rel(1)),
plot.title = element_text(hjust = .5),
strip.text.x = element_text(face = "bold", size = rel(1.4)))
mean_length = mean(final_sem$mean_len, na.rm = TRUE)
mean_logfreq = mean(final_sem$mean_logf, na.rm = TRUE)
mean_lexdec = mean(final_sem$mean_ldtz, na.rm = TRUE)
mean_concreteness = mean(final_sem$mean_conc, na.rm = TRUE)
fixed.frame <-
data.frame(expand.grid( newlogp =
seq(min(final_sem$newlogp, na.rm = TRUE),
max(final_sem$newlogp, na.rm = TRUE),
2),
mean_len = mean_length,
mean_logf = mean_logfreq,
mean_ldtz = mean_lexdec,
mean_conc = mean_concreteness)) %>%
mutate(pred = predict(m_logp, newdata = ., re.form = NA))
fixed.frame %>%
# mutate(Pathlength = factor(directedfac,
# levels = unique(directedfac),
# labels = c("1","2", "3","4",
# "5", "6", "7", "8")))%>%
ggplot(aes(x = newlogp, y = pred, group = 1))+
geom_point()+
# geom_smooth(method = "loess")+
geom_line(color = "green")+
theme_few()+
xlab("Path Length") + ylab("z-scored RT") +
ggtitle("z-scored RT for Relatedness Judgments") +
theme(axis.text = element_text(size = rel(1)),
axis.title = element_text(face = "bold", size = rel(1)),
legend.title = element_text(face = "bold", size = rel(1)),
plot.title = element_text(hjust = .5),
strip.text.x = element_text(face = "bold", size = rel(1.4)))
library(dplyr)
z_pathlength1 = final_sem %>% filter(pathlength == "1")
z_pathlength2 = final_sem %>% filter(pathlength == "2")
z_pathlength3 = final_sem %>% filter(pathlength == "3")
z_pathlength4 = final_sem %>% filter(pathlength == "4")
z_pathlength6 = final_sem %>% filter(pathlength == "6")
z_pathlength15 = final_sem %>% filter(pathlength == "15")
rawRT_p1 = sem %>% filter(pathlength == "1")
sem_firsttrim1 = sem_firsttrim %>% filter(pathlength == "15")
## aggregate per subject all IVs and DVs
meanRT = group_by(sem_firsttrim1, subject) %>%
summarise_at(vars(rt), mean)
colnames(meanRT) = c("subject", "MeanRT")
sdRT = group_by(sem_firsttrim1, subject) %>%
summarise_at(vars(rt), sd)
colnames(sdRT) = c("subject", "sdRT")
RT_agg = merge(meanRT, sdRT, by = "subject")
## merge aggregate info with long data
sem_z_1 = merge(sem_firsttrim1, RT_agg, by = "subject", all.x = T)
## person and grand-mean centered scores using original and aggregate
library(dplyr)
sem_z_1 = sem_z_1 %>% mutate(zRT = (rt - MeanRT)/sdRT)
## checking: subject level means should be zero
sub_pic = group_by(sem_z_1, subject) %>%
summarise_at(vars(zRT), mean)
```